جست‌وجوی استاد و پژوهشگر

Merry-Lynn McDonaldمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• COPD
• Cachexia
• +۵ مورد دیگر

Dr. Merry-Lynn McDonald is an Associate Professor in the Department of Medicine's Division of Pulmonary, Allergy, and Critical Care Medicine at the University of Alabama at Birmingham (UAB). She is also Director of Integrative 'Omics in the same division. Her academic roles include appointments across multiple UAB schools, including the School of Public Health and Academic Joint Departments in Genetics. Dr. McDonald holds dual faculty status in the School of Medicine and School of Public Health, reflecting her interdisciplinary focus. Education: PhD in Molecular Biophysics from Baylor College of Medicine, MS in Epidemiology from University of Saskatchewan, and BS in Biochemistry from University of Waterloo. Postdoctoral training at Brigham and Women's Hospital in Network Medicine. Research Focus: Her work centers on genetic epidemiology of complex traits, particularly COPD cachexia, osteoarthritis, and smoking-related comorbidities. She leads the McDonald Laboratory of Integrative 'Omics, leveraging omics data (genomics, proteomics, metabolomics) to uncover disease mechanisms. Key projects include the Million Veteran Program (MVP), Trans-Omics for Precision Medicine (TOPMed), and Cachexia in COPD Etiology of Low-Lean Muscle (CCELL). Grants & Funding: Active grants from NIH (K99/R00, NHLBI) and the Parker B. Francis Foundation. Her research explores network medicine approaches to COPD, osteoarthritis prevalence in veterans, and biomarker development for lung disease comorbidities. Labs/Teams: Directs the Integrative 'Omics Lab, collaborating with multidisciplinary teams across UAB's Lung Health Center, CAMBAC, and the Nutrition Obesity Research Center (NORC). The lab actively recruits postdoctoral fellows, bioinformaticians, and students with expertise in computational biology and statistical genetics.

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

Kristofer Reyesمشاهده پروفایل

دانشیار

• Applied Mathematics
• Computational Chemistry
• Materials Science
• +۶ مورد دیگر

Kristofer Reyes is an Associate Professor in the Department of Materials Design and Innovation at the University at Buffalo (School of Engineering and Applied Sciences). His research focuses on computational and statistical methods applied to materials science, particularly in developing machine learning frameworks for small-data regimes. He leads the Computational and Statistical Material Science (CSMS) Lab, which emphasizes Bayesian models, reinforcement learning, and fusion of physics-based knowledge. Education: PhD in Applied and Interdisciplinary Mathematics, University of Michigan, 2013 BS in Computer Science and Mathematics, Purdue University, 2004 Research Interests: Reyes' work bridges computation, mathematics, and materials science. Key areas include: - Autonomous experimentation systems (e.g., self-driving fluidic labs) - Bayesian optimization and decision-making under uncertainty - Machine learning for nanomaterials synthesis (e.g., perovskite nanocrystals) - Integration of physics-informed models in AI workflows - High-cost experimental design optimization Recent Achievements: SMARTDOPE paper awarded 'Best in Advanced 2023' Developed AlphaFlow for autonomous chemical synthesis Pioneered 'self-driving labs' for materials discovery Labs & Teams: CSMS Lab (134 Bell Hall) focuses on problem-fluent models for materials research. Current projects include quantum circuit optimization, bio-chemical pathway modeling, and sustainable nanomanufacturing. Collaborates with industry and national labs on AI-driven materials development.

Isabel Valera Martinezمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Isabel Valera Martínez is a full Professor of Machine Learning at the Department of Computer Science, Saarland University, and an Adjunct Faculty at the Max Planck Institute for Software Systems (MPI-SWS). She holds a PhD in Machine Learning from the University Carlos III of Madrid and has held prestigious fellowships including the Humboldt Postdoctoral Fellowship and the Minerva Fast Track Fellowship from the Max Planck Society. Her research focuses on developing interpretable, robust, and fair machine learning methods, with applications in healthcare, social systems, and consequential decision-making like hiring and loan approvals. Education: PhD in Machine Learning (2014) and MSc in Multimedia and Communications (2012) from University Carlos III of Madrid; Master’s work at Leibniz University Hannover (2009); Telecommunications Engineering from the Technical University of Cartagena (2009). Research Interests: Fairness in AI, Bayesian nonparametric models, robust machine learning, and applications in healthcare and social systems. She co-leads the ELLIS Robust Machine Learning program and the Saarbrücken AI & Machine Learning (SAM) Unit. Key Publications: Contributions to fair classification frameworks, automatic discovery of data types, and modeling psychiatric comorbidity. Notable venues include AAAI, NIPS/NeurIPS, JMLR, and ICML. Awards: Humboldt Fellowship, Minerva Fellowship, Best Paper Honorable Mention at WWW 2017. Labs & Collaborations: Active in the ELLIS Society and MPI-SWS, focusing on interdisciplinary AI applications.

• Ophthalmology
• Neurodegenerative Diseases
• Multi-Omics Analysis
• +۷ مورد دیگر

Sanjoy K. Bhattacharya is a tenured Professor of Ophthalmology at the Bascom Palmer Eye Institute, University of Miami, with secondary appointments in Biochemistry and Molecular Biology and Neuroscience. His research focuses on intraocular pressure homeostasis, optic nerve regeneration, and neurodegenerative diseases. He leads the Miami Integrative Metabolomics Research Center and serves as the graduate program director for translational programs. Dr. Bhattacharya holds a PhD from IIT-Delhi and postdoctoral training at McGill University and Cleveland Clinic. Education: PhD in Bioengineering, Indian Institute of Technology-Delhi (1997) Master's in Biotechnology and Bioengineering, Banaras Hindu University (1992, 1990) Postdoctoral Fellowships: Pharmacology (McGill University), Structural Biology (Cleveland Clinic) Research Interests: Combines multi-omics approaches with advanced mass spectrometry and NMR to study ocular neurodegenerative diseases, lipid signaling, and optic nerve regeneration. Key areas include glaucoma mechanisms, axon remyelination, and translational therapies. Awards: ARVO Silver Fellow Chancellor’s Distinguished Award (LSU) ISER Mentor of the Month Japan Predoctoral Fellowship Teaching: Integrates experimental methods with didactic teaching, emphasizing biochemistry fundamentals and entrepreneurship. Uses creative pedagogical tools like seed germination statistics and turmeric growth experiments to teach scientific principles. Labs/Teams: Director of Miami Integrative Metabolomics Research Center, leading multidisciplinary teams in lipidomics and systems biology. Active in translational research partnerships with industry.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

• Bayesian Networks
• Artificial Intelligence
• Risk Analysis
• +۳ مورد دیگر

Steven Mascaro is a Senior Research Fellow in the Department of Data Science & AI at Monash University. His research focuses on advancing Bayesian network methodologies and their application in complex domains such as public health, biosecurity, and policy analysis. He collaborates on projects like 'Fitting AI technology to complex policy problems' and 'The Development of Novel Bayesian Network and Multi-Criteria Decision Analysis Techniques.' Key research areas include causal modeling for medical decision support, risk assessment frameworks for invasive species, and expert elicitation techniques. Mascaro's work bridges theoretical AI advancements with practical implementations in healthcare and environmental management. His contributions span over 27 peer-reviewed outputs, emphasizing interdisciplinary collaboration. Projects: 2 major research initiatives funded through 2022 Publications: Over 25 articles since 2001, with recent focus on Bayesian networks in pandemic response and clinical diagnostics Expertise: Bayesian network parameterization, causal inference, and decision-support tool development His current efforts prioritize scalable AI solutions for policy challenges and improving diagnostic accuracy through causal modeling.

Joseph Romanoمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +۷ مورد دیگر

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

Catherine Jutzelerمشاهده پروفایل

استادیار

• Spinal Cord Injury
• Pain Management
• Machine Learning in Medicine
• +۳ مورد دیگر

Prof. Dr. Catherine Jutzeler is an Assistant Professor at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. Her research focuses on spinal cord injury, pain management, and the application of machine learning in medical diagnostics and rehabilitation. She specializes in developing advanced computational methods for analyzing spinal MRI scans, predicting clinical outcomes, and optimizing therapeutic interventions. Her work bridges biomedical engineering and clinical neuroscience, with a strong emphasis on translational research. Key areas include neuroimaging analysis, serological biomarker discovery, and the development of predictive models for neurological recovery. Recent studies have explored the impact of pharmacological treatments, racial disparities in biomarker responses, and biomechanical thresholds in cervical myelopathy. Prof. Jutzeler collaborates extensively on interdisciplinary projects, integrating clinical data with machine learning frameworks. She is particularly known for pioneering diffusion-based models for lumbar spine segmentation and advancing data standards in spinal cord injury research. Her contributions aim to improve diagnostic accuracy and personalize patient care in neurotrauma and chronic pain management.

Terence Ozolinsمشاهده پروفایل

دانشیار

• Developmental Toxicology
• Cardiac Birth Defects
• Ventricular Septation
• +۲ مورد دیگر

Terence Ozolins is an Associate Professor in the Department of Biomedical and Molecular Sciences at Queen's University's Faculty of Health Sciences. He holds cross-appointments with the Translational Institute of Medicine (TIME) and teaches courses in pharmacology and toxicology, including PHAR 480: Drug Discovery and Development. His research focuses on the etiology of ventricular septation defects (VSD) using rat models to understand how environmental influences during gestation cause structural heart defects and long-term postnatal cardiac dysfunction even after spontaneous repair. The lab investigates molecular mechanisms underlying teratogen-induced cardiac malformations and persistent physiological abnormalities. Publications demonstrate expertise in high-resolution fetal ultrasound imaging, molecular pathways of teratogenesis, and transgenerational effects of chemical exposures. Recent work explores AP-1 transcription factor regulation in embryotoxicity. Scientific Awards: Safety Sciences Award for Scientific Excellence (2003) Dr. Ozolins maintains collaborations with clinical researchers and previously spent 11 years in pharmaceutical industry research. Current projects examine epigenetic mechanisms in congenital heart defects and developmental origins of adult cardiovascular disease.

Chengnian Sunمشاهده پروفایل

دانشیار

• Software Engineering
• Software Reliability
• Software Security
• +۵ مورد دیگر

Chengnian Sun is an Associate Professor at the University of Waterloo's David R. Cheriton School of Computer Science. He holds a Ph.D. from the National University of Singapore (2013). His research focuses on software engineering, emphasizing software reliability, security, and developer productivity. His work spans compiler testing, program analysis, cybersecurity, and programming language tools. Key research trends include leveraging large language models (LLMs) for compiler testing and program reduction, probabilistic debugging techniques, and enhancing software security against ransomware and fuzzing attacks. His contributions address challenges in program simplification, fault localization, and vulnerability detection. Notable projects include frameworks like Perses (syntax-guided program reduction), T-Rec (language-agnostic program reduction), and tools like AddressWatcher for memory leak detection. His work bridges theoretical advancements with practical software engineering solutions. Chengnian advises on compiler reliability, cybersecurity, and developer productivity. His research has led to collaborations with industry on testing tools and security frameworks. He maintains an active lab focused on advancing software systems through rigorous analysis and innovation.

• Data Governance
• Semantic Modeling
• Knowledge Graphs
• +۵ مورد دیگر

Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.

Jun Liuمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Bayesian Methods
• +۳ مورد دیگر

Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.

• Cancer
• Clinical Trials
• Cardiovascular Disease
• +۳ مورد دیگر

Professor John Simes AO is a Professor of Clinical Epidemiology at the University of Sydney's Faculty of Medicine and holds leadership roles at the NHMRC Clinical Trials Centre (CTC), including Senior Associate Director and Co-Director of Cancer Trials. He has led over 100 multicenter clinical trials, notably the LIPID and FIELD trials, contributing to global guidelines in cardiovascular and diabetes care. His research spans cancer, cardiovascular disease, neonatal medicine, and clinical trial methodology. Education: BSc (Med), MBBS, FRACP, SM (Harvard), MD. He is a practicing Medical Oncologist at Royal Prince Alfred Hospital and Chris O'Brien Lifehouse. Research Interests: Large-scale clinical trials in oncology (e.g., gastrointestinal, genitourinary cancers), cardiovascular risk reduction, and neonatal medicine. Notable contributions include demonstrating pravastatin's survival benefits (LIPID trial) and fenofibrate's microvascular benefits (FIELD trial). Current trials include INTEGRATE (gastric cancer) and INSPIRE (aspirin in VTE). Awards: Order of Australia (2020), MOGA Cancer Achievement Award (2010), Harvard Biostatistics Alum Award (2009), and R.T. Hall Prize (1999). Advising: Supervises students like Janene Dalrymple on translational research. Grants: Holder of NHMRC Senior Principal Research Fellowship and Program Grants. Labs/Teams: Leads CTC's oncology trials group and chairs INSPIRE Steering Committee.

Laurie A. Boyerمشاهده پروفایل

استاد

• Developmental Biology
• Cardiovascular Biology
• Chromatin Remodeling
• +۴ مورد دیگر

Laurie A. Boyer is a Professor of Biology and Biological Engineering at the Massachusetts Institute of Technology (MIT), affiliated with the Koch Biology Building. She serves as Co-Undergraduate Officer and leads research on gene regulatory mechanisms in heart development and regeneration using embryonic stem cells and mouse models. Her work integrates genomic, genetic, biochemical, and cell biological approaches to understand chromatin dynamics and lineage commitment, with applications in cardiac tissue engineering and regeneration. Education: PhD, 2001, University of Massachusetts Medical School BS, 1990, Biomedical Science, Framingham State University Research Interests: Dr. Boyer’s research focuses on the interplay between chromatin remodeling, epigenetic regulation, and transcriptional networks that drive heart development. She investigates how ATP-dependent chromatin remodelers influence lineage specification and explores methods to stimulate cardiac regeneration. Her lab combines advanced genomic techniques with tissue engineering to model 3D cardiac architecture, aiming to uncover regulatory circuits underlying normal and diseased heart development. Publications: Her recent work includes breakthroughs in high-throughput expansion microscopy for scalable super-resolution imaging, metabolic modulation of mesenchymal stem cells for cartilage repair, and the role of long noncoding RNAs like Braveheart in cardiovascular lineage commitment. These studies span molecular mechanisms of chromatin dynamics to translational applications in regenerative medicine. Awards & Honors: Pew Scholars Award in the Biomedical Sciences (2008) Smith Family Award for Excellence in Biomedical Science (2009) Scientific American World’s 50 Top Leaders in Research (2006) Irvin and Helen Sizer Career Development Award (2012) Grants & Collaborations: Dr. Boyer oversees grants from organizations like the NIH and American Heart Association. She collaborates with teams on interdisciplinary projects, including tissue engineering and stem cell therapies. Her lab also develops innovative microscopy techniques to advance cellular imaging scalability. Labs & Teams: Her laboratory at MIT’s Koch Institute integrates computational biology, bioengineering, and molecular biology. The lab focuses on translating fundamental discoveries into clinical applications for heart disease and regeneration.